Dynamic Symbol-Based Video Analytics for Adaptive Object Detection
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Solution Overview
Problem
Legacy video analytics systems face challenges in dynamically implementing computer vision (CV) functions to reflect changing intended use contexts, especially in emergency situations where rapid adaptation of CV functions is necessary to detect objects-of-interest (OOI) effectively.
Innovation Solution
A dynamic symbol-based system that infers symbols representing real-world rules and signage to dynamically determine and deploy relevant CV functions for OOI detection, using a CV function library, sign detection, and machine learning methods to adapt to changing environments and contexts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a legacy video analytics system uses fixed CV functions, then the system structure is simple and stable, but the system cannot adapt to changing intended use contexts especially in emergency situations
Solution Approach 1:
The system dynamically adjusts CV functions based on detected symbols representing real-world rules. The analytics engine continuously monitors video feeds, identifies symbols (such as traffic signs, emergency indicators), and automatically modifies detection parameters and CV function selection in real-time, transforming a static system into an adaptive one that responds to environmental changes
Solution Approach 2:
Symbols detected in video feeds serve as intermediaries between the physical environment and the CV function selection process. The symbol detection module extracts meaningful symbols from video data, which then act as inputs to the analytics engine that translates these symbols into appropriate CV function configurations, enabling indirect adaptation to changing contexts
2Adaptability or versatility
If the system dynamically implements CV functions to reflect changing contexts, then the adaptability improves, but the response time and system complexity increase
Solution Approach 1:
The system pre-loads and prepares multiple CV functions and detection configurations in advance within the analytics engine. When symbols are detected in video feeds, the system can quickly switch between pre-prepared configurations rather than generating new detection parameters from scratch, significantly reducing the time required to adapt to changing contexts
Solution Approach 2:
The system changes detection parameters and CV function selection based on symbol detection results. By modifying parameters such as detection thresholds, object classes to monitor, and analysis depth according to the detected symbol type, the system achieves rapid adaptation without requiring complete system reconfiguration, thus minimizing response time
Data Source
AI summary
Disclosed herein are system, method, and computer program product embodiments for a dynamic symbol-based system for objects-of-interest (OOI) video analytics detection. Some embodiments include instantiating one or more symbolic objects associated with one or more real world rules, defining an area of interest, associating one or more CV functions with the one or more symbolic objects, and identifying one or more video sources for which to apply the one or more CV functions. Some embodiments further include executing the one or more CV functions associated with the one or more symbolic objects to process the one or more video sources.